This invention relates to the field of decision systems, and in particular, a method for developing probabilistic models, such as Bayesian networks.
Decision systems are generally used to capture expert knowledge and/or data, assisting a user of a decision system to make decisions based on the captured expertise. For example, manufacturers of automobiles, trucks, military vehicles, locomotives, aircrafts and satellites use decision systems to express diagnostic procedures. Decision systems can also be used for medical diagnosis, intelligence data analysis, web applications such as collaborative filtering and data retrieval, and for military applications such as battlefield management and enemy command structure identification and disruption.
One way to represent and/or implement decision systems is through probabilistic models, such as Bayesian networks. Bayesian networks are graphs consisting of nodes which are connected by directed arcs and are parametrized using probabilities. However, Bayesian networks do not contain closed loops or “cycles.”
The nodes of a decision Bayesian network represent observations and conclusions while the directed arcs express causal dependencies between the conclusions (e.g., failure diagnoses) and observations (e.g., tests). Bayesian networks can be used to generate decision procedures by means of an inference algorithm. Particularly, an inference algorithm can recommend a sequence of observations and, finally, a conclusion. For each step along the way, the inference algorithm can provide a ranked list of next observations based on prior observation results. The user is expected to choose the top ranking observation recommendation, but is not limited to it. A lower ranked recommendation can be selected if the user cannot or does not want to follow the top recommendation.
To provide a simple illustration, a decision system could aid an automobile mechanic with diagnosing technical problems of an automobile. For example, confronted with an automobile that fails to start, the systems could suggest to begin troubleshooting by checking the battery. Traditional decision systems using inflexible data structures (such as decision trees) often require users to adhere to a fixed sequence of suggested tests with little room for skipping tests or alternating their order. However, when using probabilistic networks, a system can support several courses of action. For example, the automobile diagnosis decision system could, in addition to suggesting checking the battery, include lower ranked observations, e.g., checking the starter, the ignition, or the fuel supply. For example, the mechanic might know that the battery tester is being used at the moment and prefer to check the spark plugs first. Depending on the outcome of this test, the system could then recommend the next best test.
Probabilistic models do not only offer increased flexibility but are also easier to modify and some classes of probabilistic models only grow linearly in size with the amount of tests contained in a decision system. Moreover, capturing expertise using a probabilistic model can be made significantly less complicated than when using inflexible data structures, such as decision trees, especially when the domain of expertise is complex and the amount of observations and conclusions is high (such as over 1000). Often, an expert must be assisted by a knowledge engineer to capture her knowledge using a probabilistic model. As a result, many experts still prefer using inflexible data structures, such as decision trees, to capture their knowledge and/or data.
Therefore, there is a need for a more efficient, less complicated method for developing probabilistic models capturing complex domains of knowledge and/or data.